Department of Mathematics, Korea University, Seoul, 02841, State, Republic of Korea.
The Institute of Basic Science, Korea University, Seoul, 02841, State, Republic of Korea.
BMC Infect Dis. 2024 Sep 27;24(1):1031. doi: 10.1186/s12879-024-09961-2.
In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India.
在本文中，我们提出了一种数值算法，以获得时变易感-未识别感染-确诊（tSUC）模型的最优流行参数。tSUC 模型是为了研究未确诊感染病例在较长时间内的流行病学而开发的。在流行参数中，由于各种因素的影响，传播率可能会显著波动或保持稳定。例如，如果早期对疫情的干预失败，传播率可能会增加，而适当的政策，包括严格的公共卫生措施，可以降低传播率。因此，我们使用给定累积确诊数据集的新确诊病例数量的线性变化点自适应地估计传播率，并基于估计的线性变化点处的传播率内插时变传播率。所提出的数值算法预处理印度的实际累积确诊病例以使其平滑，并使用预处理数据识别线性变化点。使用这些线性变化点和 tSUC 模型，它找到了最小化实际累积确诊病例与计算数值解之间最小二乘差异的最优时变参数。数值实验展示了使用所提出算法找到的最优时变参数的 tSUC 模型的数值解，验证了算法的性能。因此，所提出的数值算法计算了印度实际累积确诊病例的时变传播率，这可以作为分析印度 COVID-19 大流行的基础。
